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Copy pathsetup.py
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executable file
·52 lines (40 loc) · 1.74 KB
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from setuptools import setup, find_packages
from sys import version as pyversion
long_description = \
"""
Contains tools for:
- Building a selection function for any multi-fibre spectrograph
- Applying the selection function to data in observable or intrinsic parameter space
- Using isochrones to calculate the colour/apparent magnitude of an object given it's intrinsic properties
"""
CLASSIFIERS = ['Topic :: Scientific/Engineering :: Astronomy',
'Intended Audience :: Science/Research',
'License :: OSI Approved :: GNU General Public License v3 (GPLv3)',
'Natural Language :: English',
'Programming Language :: Python :: 2 :: Only']
setup(
name="seestar",
version=1.0,
# Project uses reStructuredText, so ensure that the docutils get
# installed or upgraded on the target machine
install_requires=['numpy', 'pandas', 'scipy', 'matplotlib', 'emcee', 'mpmath',
'scikit-learn', 'tqdm', 'healpy', 'future', 'psutil', 'dill', 'h5py'],
setup_requires=["pytest-runner"],
tests_require=["pytest"],
include_package_data=True,
package_data={'': ['*.md']
# If any package contains *.txt or *.rst files, include them:
#'': ['*.txt', '*.rst'],
# And include any *.msg files found in the 'hello' package, too:
#'hello': ['*.msg'],
},
packages = ['seestar'],
classifiers = CLASSIFIERS,
# metadata for upload to PyPI
author="Andrew Everall",
author_email="andrew.everall1995@gmail.com",
description="Selection Function package",
license="GPLv3",
url="https://github.com/aeverall/seestar", # project home page, if any
# could also include long_description, download_url, classifiers, etc.
)